• Title/Summary/Keyword: Prediction of variables

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Price Prediction of Fractional Investment Products Using LSTM Algorithm: Focusing on Musicow (LSTM 모델을 이용한 조각투자 상품의 가격 예측: 뮤직카우를 중심으로)

  • Jung, Hyunjo;Lee, Jaehwan;Suh, Jihae
    • Journal of Intelligence and Information Systems
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    • v.28 no.4
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    • pp.81-94
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    • 2022
  • Real estate and artworks were considered challenging investment targets for individual investors because of their relatively high average transaction price despite their long investment history. Recently, the so-called fractional investment, generally known as investing in a share of the ownership right for real-life assets, etc., and most investors perceive that they actually own a piece (fraction) of the ownership right through their investments, is gaining popularity. Founded in 2016, Musicow started the first service that allows users to invest in copyright fees related to music distribution. Using the LSTM algorithm, one of the deep learning algorithms, this research predict the price of right to participate in copyright fees traded in Musicow. In addition to variables related to claims such as transfer price, transaction volume of claims, and copyright fees, comprehensive indicators indicating the market conditions for music copyright fees participation, exchange rates reflecting economic conditions, KTB interest rates, and Korea Composite Stock Index were also used as variables. As a result, it was confirmed that the LSTM algorithm accurately predicts the transaction price even in the case of fractional investment which has a relatively low transaction volume.

Predicting standardized ileal digestibility of lysine in full-fat soybeans using chemical composition and physical characteristics

  • Chanwit Kaewtapee;Rainer Mosenthin
    • Animal Bioscience
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    • v.37 no.6
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    • pp.1077-1084
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    • 2024
  • Objective: The present work was conducted to evaluate suitable variables and develop prediction equations using chemical composition and physical characteristics for estimating standardized ileal digestibility (SID) of lysine (Lys) in full-fat soybeans (FFSB). Methods: The chemical composition and physical characteristics were determined including trypsin inhibitor activity (TIA), urease activity (UA), protein solubility in 0.2% potassium hydroxide (KOH), protein dispersibility index (PDI), lysine to crude protein ratio (Lys:CP), reactive Lys:CP ratio, neutral detergent fiber, neutral detergent insoluble nitrogen (NDIN), acid detergent insoluble nitrogen (ADIN), acid detergent fiber, L* (lightness), and a* (redness). Pearson's correlation (r) was computed, and the relationship between variables was determined by linear or quadratic regression. Stepwise multiple regression was performed to develop prediction equations for SID of Lys. Results: Negative correlations (p<0.01) between SID of Lys and protein quality indicators were observed for TIA (r = -0.80), PDI (r = -0.80), and UA (r = -0.76). The SID of Lys also showed a quadratic response (p<0.01) to UA, NDIN, TIA, L*, KOH, a* and Lys:CP. The best-fit model for predicting SID of Lys in FFSB included TIA, UA, NDIN, and ADIN, resulting in the highest coefficient of determination (R2 = 0.94). Conclusion: Quadratic regression with one variable indicated the high accuracy for UA, NDIN, TIA, and PDI. The multiple linear regression including TIA, UA, NDIN, and ADIN is an alternative model used to predict SID of Lys in FFSB to improve the accuracy. Therefore, multiple indicators are warranted to assess either insufficient or excessive heat treatment accurately, which can be employed by the feed industry as measures for quality control purposes to predict SID of Lys in FFSB.

Estimating Stature and Weight from Anthropometry for the Elderly Who are Limited in Mobility (신체계측방법에 의한 거동이 제한된 노인들의 신장과 체중추정)

  • 한경희
    • Journal of Nutrition and Health
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    • v.28 no.1
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    • pp.71-83
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    • 1995
  • The purpose of the study was to develop generalized equations for estimating stature and weight for the nonambulatory elderly persons. Height weight recumbent knee height total ann length, midarm, waist and calf circumferences, triceps and subscapular skinfolds were measured from over 60 years old 315 ambulatory elderly. The equations to predict stature and weight were derived from participants in the validation sample and were applied to the participants in the cross-validation to test the accuracy and validity of equations. Stature and weight were significantly and negatively associated with age of women and similar patterns observed in men but associated to a slight degree. Knee height and total arm length were highly correlated with stature but the majority of the variances in stature was accounted for by knee height for both the men and women. In men, waist circumference was the most significantly correlated with weight and am, calf circumferences and so forth. But in women arm circumference was the highest then waist and calf circumference in order. The possible predictor variables to estimate of stature were knee height total arm length and age for both elderly men and women. Predictor variables to estimate of weight were recumbent measures of waist am, calf circumferences and knee height for both sexes. Inclusion of skinfold thickness measurements did not improve the prediction power of estimation for weight. When both equations developed from the present study and Chumlea's study were applied to cross-valida-tions samples, the equations derived from present study showed better accuracy and validity. The presentation of prediction equations using two, three, or four recommended measurements allows the selection of an equation based upon the measurements that are possible to collect on an individual basis.

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Prediction model of hypercholesterolemia using body fat mass based on machine learning (머신러닝 기반 체지방 측정정보를 이용한 고콜레스테롤혈증 예측모델)

  • Lee, Bum Ju
    • The Journal of the Convergence on Culture Technology
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    • v.5 no.4
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    • pp.413-420
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    • 2019
  • The purpose of the present study is to develop a model for predicting hypercholesterolemia using an integrated set of body fat mass variables based on machine learning techniques, beyond the study of the association between body fat mass and hypercholesterolemia. For this study, a total of six models were created using two variable subset selection methods and machine learning algorithms based on the Korea National Health and Nutrition Examination Survey (KNHANES) data. Among the various body fat mass variables, we found that trunk fat mass was the best variable for predicting hypercholesterolemia. Furthermore, we obtained the area under the receiver operating characteristic curve value of 0.739 and the Matthews correlation coefficient value of 0.36 in the model using the correlation-based feature subset selection and naive Bayes algorithm. Our findings are expected to be used as important information in the field of disease prediction in large-scale screening and public health research.

Prediction Performance of Ocean Temperature and Salinity in Global Seasonal Forecast System Version 5 (GloSea5) on ARGO Float Data

  • Jieun Wie;Jae-Young Byon;Byung-Kwon Moon
    • Journal of the Korean earth science society
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    • v.45 no.4
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    • pp.327-337
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    • 2024
  • The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.

Development of a Predictive Model forOccupational Disability Grades Using Workers'Compensation Insurance Data (산재보험 빅데이터를 활용한 장해등급 예측 모델 개발)

  • Choi, Keunho;Kim, Min Jeong;Lee, Jeonghwa
    • The Journal of Information Systems
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    • v.33 no.3
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    • pp.187-205
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    • 2024
  • Purpose A prediction model for occupational injuries can support more proactive, efficient, and effective policy-making. This study aims to develop a model that predicts the severity of occupational injuries, classified into 15 disability grades in South Korea, using machine learning techniques applied to COMWEL data. The primary goal is to improve prediction accuracy, offering an advanced tool for early intervention and evidence-based policy implementation. Design/methodology/approach The data analyzed in this study consists of 290,157 administrative records of occupational injury cases collected between 2018 and 2020 by the Korea Workers' Compensation & Welfare Service, based on the 'Workers' Compensation Insurance Application Form' submitted for occupational injury treatment. Four machine learning models - Decision Tree, DNN, XGBoost, and LightGBM - were developed and their performances compared to identify the optimal model. Additionally, the Permutation Feature Importance (PFI) method was used to assess the relative contribution of each variable to the model's performance, helping to identify key variables. Findings The DNN algorithm achieved the lowest Mean Absolute Error (MAE) of 0.7276. Key variables for predicting disability grades included the severity index, primary disease code, primary disease site, age at the time of the injury, and industry type. These findings highlight the importance of early policy intervention and emphasize the role of both medical and socioeconomic factors in model predictions. The academic and policy implications of these results were also discussed.

Prediction Models for the Stiffness and the Strength of a Double Angle Connection Subjected to Tension (축방향 인장력을 받는 더블앵글 접합부의 강성 및 강도 예측모델)

  • Yang, Jae Guen;Lee, Gil Young;Cheon, Ji Won
    • Journal of Korean Society of Steel Construction
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    • v.19 no.2
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    • pp.201-210
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    • 2007
  • Double-angle connections should be designed with enough stiffness and strength to properly resist various applied loads. Therefore, structural engineers should be able to predict some influential variables and take their effects into account in design. This study was performed to establish the effects of the number of bolts and bolt gage distance on the stiffness and strength of a double-angle connection under tension. Six experimental tests were conducted to describe the effects of these variables by comparing load-displacement relationship curves. In addition, two prediction models were proposed to estimate the initial stiffness and the maximum allowable tensile load based on the results of experimental tests. In the development of these prediction models, the effect of prying action was considered.

Prediction of arrhythmia using multivariate time series data (다변량 시계열 자료를 이용한 부정맥 예측)

  • Lee, Minhai;Noh, Hohsuk
    • The Korean Journal of Applied Statistics
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    • v.32 no.5
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    • pp.671-681
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    • 2019
  • Studies on predicting arrhythmia using machine learning have been actively conducted with increasing number of arrhythmia patients. Existing studies have predicted arrhythmia based on multivariate data of feature variables extracted from RR interval data at a specific time point. In this study, we consider that the pattern of the heart state changes with time can be important information for the arrhythmia prediction. Therefore, we investigate the usefulness of predicting the arrhythmia with multivariate time series data obtained by extracting and accumulating the multivariate vectors of the feature variables at various time points. When considering 1-nearest neighbor classification method and its ensemble for comparison, it is confirmed that the multivariate time series data based method can have better classification performance than the multivariate data based method if we select an appropriate time series distance function.

Development of a Accident Frequency Prediction Model at Rural Multi-Lane Highways (지방부 다차로 도로구간에서의 사고 예측모형 개발 (대도시권 외곽 및 구릉지 특성의 도로구간 중심으로))

  • Lee, Dong-Min;Kim, Do-Hun;Seong, Nak-Mun
    • Journal of Korean Society of Transportation
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    • v.27 no.4
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    • pp.207-215
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    • 2009
  • Generally, traffic accidents can be influenced by variables driving conditions including geometric, roadside design, and traffic conditions. Under the circumstance, homogeneous roadway segments were firstly identified using typical geometric variables obtained from field data collections in this study. These field data collections were conducted at highways located in several areas having various regional conditions for examples, outside metropolitan city; level and rolling rural areas. Due to many zero cells in crash database, a Zero Inflated Poisson model was used to develop crash prediction model to overestimated results in this study. It was found that EXPO, radius, grade, guardrail, mountainous terrain, crosswalk and bus-stop have statistically significant influence on vehicle to vehicle crashes at rural multi-lane roadway segments.

Invariant causal prediction for time series data: Application to won dollar exchange rate data (시계열 자료에서 불변하는 인과성 탐색: 원-달러 환율 데이터에 적용)

  • Kim, Mijeong
    • The Korean Journal of Applied Statistics
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    • v.34 no.5
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    • pp.837-848
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    • 2021
  • Evaluating or predicting the effectiveness of economic policies is an important issue, but it is difficult to find an economic variable which causes a significant result because there are numerous variables that cannot be taken into account. A randomized controlled experiment is the best way to investigate causality, but it is not realistically possible to control through randomization and intervention in time series data such as macroeconomic data. Although some analysis methods have been proposed to find causality, the methods such as Granger causality method and Chow test are insufficient to explain causality. Recently, Pfister et al. (2019) proposed invariant causal prediction methods which can be applicable in time series data. In this paper, we introduce the method of Pfister et al. (2019) and use the method to find macroeconomic variables invariantly affecting the won-dollar exchange rate.